2 Abstract One of the primary competencies for a self-driving car is to build a map of its environment and estimates its pose with respect to this map, so that it can plan its path and navigate freely without any human interventions. A multi-camera system is a good choice as the main sensor for a self-driving car because as compared to the commonly used Lidar sensor, cameras are lower in cost, easier to maintain and images provide richer information. In addition, there is the flexibility to configure the multiple cameras in a way that maximizes the fieldof-view. In this thesis, a suite of novel algorithms is proposed to do mapping and pose estimation efficiently and robustly with a multi-camera system on a self-driving car. Mapping begins with motion estimation where the trajectory of the car is estimated. Existing algorithms for motion estimation with a multi-camera system are computationally inefficient for practical applications. By applying the Ackermann and planar constraints to reduce the parameters needed for motion estimation, we propose two efficient minimal algorithms - 2-point and 3-point algorithms. Both the Ackermann and planar constraints are based on the assumption that the car traverses on flat ground. While this assumption holds true for most cases, it is violated when the car negotiates a ramp etc. Two additional algorithms - minimal 4-point and linear 8-point algorithms are proposed to do full 6 degree-of-freedom motion estimation with known absolute roll and pitch angles. Errors from motion estimation are accumulated and eventually lead to loopclosure errors when the car revisits previously visited locations. A loop-closure is identified with visual place recognition and verified by geometric verification. The threshold for geometric verification is critical for verifying correct loop-closures but is chosen empirically. An algorithm is proposed to self-learn the threshold for geometric verification. Loop-closure errors are minimized with non-linear least squares that fails in the presence of outliers. A robust viii

3 loop-closure algorithm is proposed to alleviated the detrimental influences from outliers. Once we have the map, pose estimation can be done to localize the selfdriving car. Existing pose estimation algorithms for a single camera cannot be applied on our multi-camera system. We propose a new formulation of 3-point minimal solution based on the Plücker coordinates to solve the pose estimation problem of a multi-camera system. ix

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